In the field of process simulation, simulator design is typically based on two different model types: first principle-based and empirical data-based models. A first principle-based model, also called a high-fidelity model, models equipment and processes based on first principle physical laws, such as mass, energy, and momentum conservation laws. First principle-based models describing a physical process are often complex and may be expressed using partial differential equations and/or differential algebraic equations. These equations may describe process or equipment properties and/or changes in those properties. In many first principle-based models, equations are modular to model specific pieces of equipment and/or processes in a multi-equipment or multi-process system. Thus, equipment and/or processes can be easily changed and/or updated in the model by replacing equations in the model with equations corresponding to the changed and/or updated equipment and/or processes. However, first principle-based models are subject to modeling errors due to the inability of first principle-based models to account for uncertainty surrounding the actual characteristics or properties of process equipment.
On the other hand, empirical data-based models, also commonly called black-box models, generate modeling formulas or equations by applying test inputs to an actual process system in accordance with a designed experiment and measuring test outputs corresponding to the test inputs. Based on the inputs and outputs, equations to determine a relationship between the inputs and outputs are generated to model the process or equipment. In this approach, the empirical equations may be easier to obtain than first principle-based equations, and dynamic transient phenomena may be better captured and represented in the empirical equations than first principle-based equations. However, special experiments must be designed, implemented, and executed to acquire accurate and diverse data sufficient to acquire the empirical data used to develop the model. Further, when equipment is changed or replaced, new empirical models must be developed, which is time consuming and costly.
Regardless of the type of process modeling approach used, a process system model often needs tuning and/or adjustment. Such tuning and adjustment are typically performed by a trial and error method that may be repeated multiples times to reflect drift in process data due to equipment aging or fatigue.